• Weiterentwicklung leistungsstarker Computer-Vision-Architekturen zur Erkennung und Diagnose medizinischer Befunde in komplexen radiologischen Datensätzen (MRT, CT). • Konzeption und Implementierung rigoroser Validierungsframeworks zur Sicherstellung von Modellrobustheit, klinischer Wirksamkeit und Konformität mit Standards der Medizinprodukte-Zertifizierung. • Entwicklung von Lösungen mit klinischem Vertrauen im Fokus, insbesondere durch Modellinterpretierbarkeit und Unsicherheitsquantifizierung, um umsetzbare Erkenntnisse für medizinisches Fachpersonal zu liefern. • Optimierung skalierbarer ML-Pipelines in einer modernen Docker- und AWS-Umgebung, um einen reibungslosen Übergang von experimenteller Forschung zu produktionsreifen Deployments zu gewährleisten.

Role Description IDC is building the next generation of AI-powered intelligence platforms that transform how technology decisions get made. We are looking for a Senior AI Quality/Evaluation Engineer to establish the evaluation function for the platform's AI systems. This is a solo function initially. You will design and build the evaluation infrastructure that ensures the platform produces accurate, well-sourced, high-quality responses. You will be the first hire in this function and must be able to operate independently, defining your own roadmap and building from scratch. The platform's credibility depends on the quality of its AI-generated intelligence. You will build the automated test suites, regression detection systems, and evaluation frameworks that catch quality issues before they reach users. You will work closely with the product team to translate quality criteria into measurable, automatable test scenarios, and with the AI engineering team to ensure that every pipeline change is evaluated against rigorous standards. What You’ll Do - Design and build the evaluation infrastructure that ensures the platform's AI systems produce accurate, well-sourced, high-quality responses. - Build automated test suites that validate answer quality across agent pipeline changes. - Develop regression detection systems that catch quality degradation before it reaches users. - Create evaluation frameworks that measure response accuracy, citation correctness, and source quality. - Work closely with the product team to translate quality criteria into measurable, automatable test scenarios. - Build cost and latency monitoring that tracks the operational efficiency of AI pipeline execution. - Define evaluation standards and practices that scale as the platform and team grow. Qualifications - 6+ years of software engineering experience, with significant work in testing infrastructure, ML evaluation, or quality systems. - Experience building evaluation or testing frameworks for LLM-based or ML-based systems. - Understanding of how to measure response quality for generative AI: accuracy, groundedness, citation correctness, relevance. - Proficiency in Python. - Ability to operate independently and define your own roadmap. - Experience working at the intersection of engineering and product, translating qualitative quality criteria into quantitative measurements. - Experience with LLM evaluation frameworks (e.g., RAGAS, DeepEval, or custom). - Familiarity with LLM observability tools (e.g., Langfuse, LangSmith, Weights & Biases). - Background in statistical methods for quality measurement (significance testing, distribution analysis). - Experience building A/B testing or experimentation infrastructure. - Background in search relevance evaluation or information retrieval metrics. Benefits - 15 vacation days per year (increases with tenure; carryover allowed). - 10 paid sick days per year. - 1 week paid new parenting leave. - Flexible work options (remote, part-time, flexible hours). - Health, dental, vision, and paramedical coverage for you and your family. - $1,600 annual healthcare spending account. - Employee Assistance Program for counseling and support. - Best Doctors medical second opinions. - Life, AD&D, and long-term disability insurance. - Retirement savings plan with company match (up to 4% of salary). - $75/month technology allowance for home office or phone expenses. - Company-paid cell phone plan.

• Design, build, and scale experimentation and causal inference services • Develop and maintain advanced statistical and ML modules • Build and extend RESTful APIs using FastAPI • Design and optimize large-scale data pipelines using PySpark

• Lead the development, testing, and deployment of ML models and pipelines, with a focus on scalability and integration into production systems. • Design and refine GenAI/LLM-based models to streamline and automate clinical trial operations, from data gathering to real-time performance monitoring. • Partner with clinicians, informaticists, data scientists, and engineers to build solutions aligned with Paradigm’s mission and goals. • Drive improvements in model deployment infrastructure, develop monitoring tools, and refine model performance to ensure robust production-level reliability. • Mentor junior ML engineers, contributing to team knowledge-sharing and establishing best practices for data science and machine learning. • Present complex technical insights and results to both technical and non-technical stakeholders, advocating for data science-driven strategies that align with business objectives.